The research presents a process for applying data mining techniques on dental medical records comprised of oral conditions and different dental procedures that are performed on various patients. The dental expert decides to pursue a set of procedures based on the examination and diagnostics. Digital dentistry is becoming more and more active now, hence this research addresses the issues in exploiting the digital data at its potential like heterogeneous data gathering, access restrictions or inadequate patient data and lack of expert systems to utilize the data. It proposes a way to deal with the dental medical records and apply data mining. Having gathered the dental data and prepared it through pre-processing techniques, unsupervised learning techniques were applied to perform clustering in order to discover interesting patterns and assigning these a label class. Mostly the patients lie in the mild and moderate dental patient's class. The most common problem that is being noticed in patients is tooth cavity with a treatment named "resin-based composite-one surface, posterior". Using this labelled data set, supervised learning algorithms were applied to train and test the data for predicting the targeted class accurately. A comparison between classification algorithms based on their accuracy was made to filter out the best outcome. An expert system has also been developed to support the idea, ease up the decision making process and automate the manual practices that are being used. It provides quick recommendations to the medical expert in examining the patient depending upon the diagnosis. Research reveals that decision tree runs better than others on our data set with highest accuracy in predicting the Patients' targeted classes.
展开▼
